Predicting Multitasking in Manual and Automated Driving with Optimal Supervisory Control
This work addresses the problem of distracted driving due to in-car technologies for automotive safety researchers and designers, but it is incremental as it builds on existing supervisory control theory.
The paper tackled predicting human multitasking behavior in driving by developing a computational cognitive model based on optimal supervisory control theory, which simulates adaptations to driving demands, interactive tasks, and automation levels, validated against empirical datasets.
Modern driving involves interactive technologies that can divert attention, increasing the risk of accidents. This paper presents a computational cognitive model that simulates human multitasking while driving. Based on optimal supervisory control theory, the model predicts how multitasking adapts to variations in driving demands, interactive tasks, and automation levels. Unlike previous models, it accounts for context-dependent multitasking across different degrees of driving automation. The model predicts longer in-car glances on straight roads and shorter glances during curves. It also anticipates increased glance durations with driver aids such as lane-centering assistance and their interaction with environmental demands. Validated against two empirical datasets, the model offers insights into driver multitasking amid evolving in-car technologies and automation.